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HBase is an open-source, column-oriented distributed database system in Hadoop environment. Apache HBase is needed for real-time Big Data applications. The tables present in HBase consists of billions of rows having millions of columns.

Hbase is built for low latency operations, which is having some specific features compared to traditional relational models.

In this tutorial- you will learn,

Storage Mechanism in Hbase

HBase is a column-oriented database and data is stored in tables. The tables are sorted by RowId. As shown below, HBase has RowId, which is the collection of several column families that are present in the table.

HBase Architecture, Data Flow, and Use cases

The column families that are present in the schema are key-value pairs. If we observe in detail each column family having a multiple numbers of columns. The column values stored in to disk memory. Each cell of the table has its own Meta data like time stamp and other information.

Coming to HBase the following are the key terms representing table schema

  • Table: Collection of rows present.
  • Row: Collection of column families.
  • Column Family: Collection of columns.
  • Column: Collection of key-value pairs.
  • Namespace: Logical grouping of tables.
  • Cell: A {row, column, version} tuple exactly specifies a cell definition in HBase.

Column-oriented and Row oriented storages

Column and Row oriented storages differ in their storage mechanism. As we all know traditional relational models store data in terms of row-based format like in terms of rows of data. Column-oriented storages store data tables in terms of columns and column families.

The following Table gives some key differences between these two storages

Column-oriented Database Row oriented Database
  • When the situation comes to process and analytics we use this approach. Such as Online Analytical Processing and it's applications.
  • Online Transactional process such as banking and finance domains use this approach.
  • The amount of data that can able to store in this model is very huge like in terms of petabytes
  • It is designed for a small number of rows and columns.

What HBase consists of

HBase consists of following elements,

  • Set of tables
  • Each table with column families and rows
  • Each table must have an element defined as Primary Key.
  • Row key acts as a Primary key in HBase.
  • Any access to HBase tables uses this Primary Key
  • Each column present in HBase denotes attribute corresponding to object

HBase Architecture and its Important Components

HBase Architecture, Data Flow, and Use cases

HBase architecture consists mainly of four components

  • HMaster
  • HRegionserver
  • HRegions
  • Zookeeper


HMaster is the implementation of Master server in HBase architecture. It acts like monitoring agent to monitor all Region Server instances present in the cluster and acts as an interface for all the metadata changes. In a distributed cluster environment, Master runs on NameNode. Master runs several background threads.

The following are important roles performed by HMaster in HBase.

  • Plays a vital role in terms of performance and maintaining nodes in the cluster.
  • HMaster provides admin performance and distributes services to different region servers.
  • HMaster assigns regions to region servers.
  • HMaster has the features like controlling load balancing and failover to handle the load over nodes present in the cluster.
  • When a client wants to change any schema and to change any Metadata operations, HMaster takes responsibility for these operations.

Some of the methods exposed by HMaster Interface are primarily Metadata oriented methods.

  • Table ( createTable, removeTable, enable, disable)
  • ColumnFamily (add Column, modify Column)
  • Region (move, assign)

The client communicates in a bi-directional way with both HMaster and ZooKeeper. For read and write operations, it directly contacts with HRegion servers. HMaster assigns regions to region servers and in turn check the health status of region servers.

In entire architecture, we have multiple region servers. Hlog present in region servers which are going to store all the log files.

HRegions Servers:

When Region Server receives writes and read requests from the client, it assigns the request to a specific region, where actual column family resides. However, the client can directly contact with HRegion servers, there is no need of HMaster mandatory permission to the client regarding communication with HRegion servers. The client requires HMaster help when operations related to metadata and schema changes are required.

HRegionServer is the Region Server implementation. It is responsible for serving and managing regions or data that is present in distributed cluster. The region servers run on Data Nodes present in the Hadoop cluster.

HMaster can get into contact with multiple HRegion servers and performs the following functions.

  • Hosting and managing regions
  • Splitting regions automatically
  • Handling read and writes requests
  • Communicating with the client directly


HRegions are the basic building elements of HBase cluster that consists of the distribution of tables and are comprised of Column families. It contains multiple stores, one for each column family. It consists of mainly two components, which are Memstore and Hfile.

Data flow in HBase

HBase Architecture, Data Flow, and Use cases

Write and Read operations

The Read and Write operations from Client into Hfile can be shown in below diagram.

Step 1) Client wants to write data and in turn first communicates with Regions server and then regions

Step 2) Regions contacting memstore for storing associated with the column family

Step 3) First data stores into Memstore, where the data is sorted and after that it flushes into HFile. The main reason for using Memstore is to store data in Distributed file system based on Row Key. Memstore will be placed in Region server main memory while HFiles are written into HDFS.

Step 4) Client wants to read data from Regions

Step 5) In turn Client can have direct access to Mem store, and it can request for data.

Step 6) Client approaches HFiles to get the data. The data are fetched and retrieved by the Client.

Memstore holds in-memory modifications to the store. The hierarchy of objects in HBase Regions is as shown from top to bottom in below table.

Table HBase table present in the HBase cluster
Region HRegions for the presented tables
Store It store per ColumnFamily for each region for the table
  • Memstore for each store for each region for the table
  • It sorts data before flushing into HFiles
  • Write and read performance will increase because of sorting
StoreFile StoreFiles for each store for each region for the table
Block Blocks present inside StoreFiles


In Hbase, Zookeeper is a centralized monitoring server which maintains configuration information and provides distributed synchronization. Distributed synchronization is to access the distributed applications running across the cluster with the responsibility of providing coordination services between nodes. If the client wants to communicate with regions, the servers client has to approach ZooKeeper first.

It is an open source project, and it provides so many important services.

Services provided by ZooKeeper

  • Maintains Configuration information
  • Provides distributed synchronization
  • Client Communication establishment with region servers
  • Provides ephemeral nodes for which represent different region servers
  • Master servers usability of ephemeral nodes for discovering available servers in the cluster
  • To track server failure and network partitions

Master and HBase slave nodes ( region servers) registered themselves with ZooKeeper. The client needs access to ZK(zookeeper) quorum configuration to connect with master and region servers.

During a failure of nodes that present in HBase cluster, ZKquoram will trigger error messages, and it starts to repair the failed nodes.


HDFS is Hadoop distributed file system, as the name implies it provides distributed environment for the storage and it is a file system designed in a way to run on commodity hardware. It stores each file in multiple blocks and to maintain fault tolerance, the blocks are replicated across Hadoop cluster.

HDFS provides a high degree of fault –tolerance and runs on cheap commodity hardware. By adding nodes to the cluster and performing processing & storing by using the cheap commodity hardware, it will give client better results as compared to existing one.

In here, the data stored in each block replicates into 3 nodes any in case when any node goes down there will be no loss of data, it will have proper backup recovery mechanism.

HDFS get in contact with the HBase components and stores large amount of data in distributed manner.

HBase vs. HDFS

HBase runs on top of HDFS and Hadoop. Some key differences between HDFS and HBase are in terms of data operations and processing.



  • Low latency operations
  • High latency operations
  • Random reads and writes
  • Write once Read many times
  • Accessed through shell commands, client API in java, REST, Avro or Thrift
  • Primarly accessed through MR (Map Reduce) jobs
  • Storage and process both can be perform
  • It's only for storage areas

Some typical IT industrial applications use HBase operations along with Hadoop. Applications include stock exchange data, online banking data operations, and processing Hbase is best-suited solution method.


Hbase is one of NoSql column-oriented distributed database available in apache foundation. HBase gives more performance for retrieving fewer records rather than Hadoop or Hive. It's very easy to search for given any input value because it supports indexing, transactions, and updating.

We can perform online real-time analytics using Hbase integrated with Hadoop eco system. It has an automatic and configurable sharding for datasets or tables, and provides restful API's to perform the MapReduce jobs.



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